{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "77d53341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Annotation',\n",
       " 'Arrow',\n",
       " 'Artist',\n",
       " 'AutoLocator',\n",
       " 'Axes',\n",
       " 'Button',\n",
       " 'Circle',\n",
       " 'Figure',\n",
       " 'FigureCanvasBase',\n",
       " 'FixedFormatter',\n",
       " 'FixedLocator',\n",
       " 'FormatStrFormatter',\n",
       " 'Formatter',\n",
       " 'FuncFormatter',\n",
       " 'GridSpec',\n",
       " 'IndexLocator',\n",
       " 'Line2D',\n",
       " 'LinearLocator',\n",
       " 'Locator',\n",
       " 'LogFormatter',\n",
       " 'LogFormatterExponent',\n",
       " 'LogFormatterMathtext',\n",
       " 'LogLocator',\n",
       " 'MaxNLocator',\n",
       " 'MouseButton',\n",
       " 'MultipleLocator',\n",
       " 'Normalize',\n",
       " 'NullFormatter',\n",
       " 'NullLocator',\n",
       " 'Number',\n",
       " 'PolarAxes',\n",
       " 'Polygon',\n",
       " 'Rectangle',\n",
       " 'ScalarFormatter',\n",
       " 'Slider',\n",
       " 'Subplot',\n",
       " 'SubplotSpec',\n",
       " 'Text',\n",
       " 'TickHelper',\n",
       " 'Widget',\n",
       " '_INSTALL_FIG_OBSERVER',\n",
       " '_IP_REGISTERED',\n",
       " '_IoffContext',\n",
       " '_IonContext',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " '_api',\n",
       " '_auto_draw_if_interactive',\n",
       " '_backend_mod',\n",
       " '_copy_docstring_and_deprecators',\n",
       " '_get_backend_mod',\n",
       " '_get_required_interactive_framework',\n",
       " '_interactive_bk',\n",
       " '_log',\n",
       " '_pylab_helpers',\n",
       " '_setup_pyplot_info_docstrings',\n",
       " '_warn_if_gui_out_of_main_thread',\n",
       " '_xkcd',\n",
       " 'acorr',\n",
       " 'angle_spectrum',\n",
       " 'annotate',\n",
       " 'arrow',\n",
       " 'autoscale',\n",
       " 'autumn',\n",
       " 'axes',\n",
       " 'axhline',\n",
       " 'axhspan',\n",
       " 'axis',\n",
       " 'axline',\n",
       " 'axvline',\n",
       " 'axvspan',\n",
       " 'bar',\n",
       " 'bar_label',\n",
       " 'barbs',\n",
       " 'barh',\n",
       " 'bone',\n",
       " 'box',\n",
       " 'boxplot',\n",
       " 'broken_barh',\n",
       " 'cbook',\n",
       " 'cla',\n",
       " 'clabel',\n",
       " 'clf',\n",
       " 'clim',\n",
       " 'close',\n",
       " 'cm',\n",
       " 'cohere',\n",
       " 'colorbar',\n",
       " 'colormaps',\n",
       " 'connect',\n",
       " 'contour',\n",
       " 'contourf',\n",
       " 'cool',\n",
       " 'copper',\n",
       " 'csd',\n",
       " 'cycler',\n",
       " 'delaxes',\n",
       " 'disconnect',\n",
       " 'docstring',\n",
       " 'draw',\n",
       " 'draw_all',\n",
       " 'draw_if_interactive',\n",
       " 'errorbar',\n",
       " 'eventplot',\n",
       " 'figaspect',\n",
       " 'figimage',\n",
       " 'figlegend',\n",
       " 'fignum_exists',\n",
       " 'figtext',\n",
       " 'figure',\n",
       " 'fill',\n",
       " 'fill_between',\n",
       " 'fill_betweenx',\n",
       " 'findobj',\n",
       " 'flag',\n",
       " 'functools',\n",
       " 'gca',\n",
       " 'gcf',\n",
       " 'gci',\n",
       " 'get',\n",
       " 'get_backend',\n",
       " 'get_cmap',\n",
       " 'get_current_fig_manager',\n",
       " 'get_figlabels',\n",
       " 'get_fignums',\n",
       " 'get_plot_commands',\n",
       " 'get_scale_names',\n",
       " 'getp',\n",
       " 'ginput',\n",
       " 'gray',\n",
       " 'grid',\n",
       " 'hexbin',\n",
       " 'hist',\n",
       " 'hist2d',\n",
       " 'hlines',\n",
       " 'hot',\n",
       " 'hsv',\n",
       " 'importlib',\n",
       " 'imread',\n",
       " 'imsave',\n",
       " 'imshow',\n",
       " 'inferno',\n",
       " 'inspect',\n",
       " 'install_repl_displayhook',\n",
       " 'interactive',\n",
       " 'ioff',\n",
       " 'ion',\n",
       " 'isinteractive',\n",
       " 'jet',\n",
       " 'legend',\n",
       " 'locator_params',\n",
       " 'logging',\n",
       " 'loglog',\n",
       " 'magma',\n",
       " 'magnitude_spectrum',\n",
       " 'margins',\n",
       " 'matplotlib',\n",
       " 'matshow',\n",
       " 'minorticks_off',\n",
       " 'minorticks_on',\n",
       " 'mlab',\n",
       " 'new_figure_manager',\n",
       " 'nipy_spectral',\n",
       " 'np',\n",
       " 'pause',\n",
       " 'pcolor',\n",
       " 'pcolormesh',\n",
       " 'phase_spectrum',\n",
       " 'pie',\n",
       " 'pink',\n",
       " 'plasma',\n",
       " 'plot',\n",
       " 'plot_date',\n",
       " 'plotting',\n",
       " 'polar',\n",
       " 'prism',\n",
       " 'psd',\n",
       " 'quiver',\n",
       " 'quiverkey',\n",
       " 'rc',\n",
       " 'rcParams',\n",
       " 'rcParamsDefault',\n",
       " 'rcParamsOrig',\n",
       " 'rc_context',\n",
       " 'rcdefaults',\n",
       " 'rcsetup',\n",
       " 're',\n",
       " 'register_cmap',\n",
       " 'rgrids',\n",
       " 'savefig',\n",
       " 'sca',\n",
       " 'scatter',\n",
       " 'sci',\n",
       " 'semilogx',\n",
       " 'semilogy',\n",
       " 'set_cmap',\n",
       " 'set_loglevel',\n",
       " 'setp',\n",
       " 'show',\n",
       " 'specgram',\n",
       " 'spring',\n",
       " 'spy',\n",
       " 'stackplot',\n",
       " 'stairs',\n",
       " 'stem',\n",
       " 'step',\n",
       " 'streamplot',\n",
       " 'style',\n",
       " 'subplot',\n",
       " 'subplot2grid',\n",
       " 'subplot_mosaic',\n",
       " 'subplot_tool',\n",
       " 'subplots',\n",
       " 'subplots_adjust',\n",
       " 'summer',\n",
       " 'suptitle',\n",
       " 'switch_backend',\n",
       " 'sys',\n",
       " 'table',\n",
       " 'text',\n",
       " 'thetagrids',\n",
       " 'threading',\n",
       " 'tick_params',\n",
       " 'ticklabel_format',\n",
       " 'tight_layout',\n",
       " 'time',\n",
       " 'title',\n",
       " 'tricontour',\n",
       " 'tricontourf',\n",
       " 'tripcolor',\n",
       " 'triplot',\n",
       " 'twinx',\n",
       " 'twiny',\n",
       " 'uninstall_repl_displayhook',\n",
       " 'violinplot',\n",
       " 'viridis',\n",
       " 'vlines',\n",
       " 'waitforbuttonpress',\n",
       " 'winter',\n",
       " 'xcorr',\n",
       " 'xkcd',\n",
       " 'xlabel',\n",
       " 'xlim',\n",
       " 'xscale',\n",
       " 'xticks',\n",
       " 'ylabel',\n",
       " 'ylim',\n",
       " 'yscale',\n",
       " 'yticks']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "dir(plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "513fd216",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12e788a09a0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8d0ee28b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12e7813b880>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4,5],[1,2,3,6,10],'g*',label='Green Star')\n",
    "plt.plot([2,5,6,8,9],[5,3,7,9,11],'bD', label=\"Blue Diamond\")\n",
    "plt.title('Scaterplot With Legend')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.legend(loc='best')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c4737de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12e7a22af40>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3)) # 长宽比\n",
    "plt.xlim(0,12) # 坐标轴限制区间\n",
    "plt.ylim(0,12)\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False # 坐标轴有负号时可以显示负号\n",
    "plt.plot([1,2,3,4,5],[1,2,3,6,10],'g*',label='绿星')\n",
    "plt.plot([2,5,6,8,9],[5,3,7,9,11],'bD', label=\"蓝钻\")\n",
    "plt.title('带图例的散点图')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.legend(loc='best')\n",
    "# 绘制网格线\n",
    "# plt.grid(color='r', linestyle='--', linewidth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c0db6410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(1,2,figsize=(6,4),sharey=True,dpi=120)\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False # 坐标轴有负号时可以显示负号\n",
    "ax1.plot([1,2,3,4,5],[1,2,3,6,10],'g*',label='绿星')\n",
    "ax2.plot([2,5,6,8,9],[5,3,7,9,11],'bD', label=\"蓝钻\")\n",
    "ax1.set_title('绿星散点图')\n",
    "ax2.set_title('蓝钻散点图')\n",
    "ax1.set_xlabel('X')\n",
    "ax2.set_xlabel('Y')\n",
    "ax1.set_ylabel('Y')\n",
    "ax2.set_ylabel('Y')\n",
    "ax1.set_xlim(0,12)\n",
    "ax2.set_xlim(0,12)\n",
    "ax1.set_ylim(0,12)\n",
    "ax2.set_ylim(0,12)\n",
    "\n",
    "# 也可以用下面的方式设置\n",
    "#ax1.set(title='绿星', xlabel='X', ylabel='Y', xlim=(0,12),  ylim=(0,12))\n",
    "\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "628a9a65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from numpy.random import seed, randint\n",
    "seed(10)\n",
    "# 创建figure，subplots\n",
    "fig, axes = plt.subplots(2,2, figsize=(10,6), sharex=True, sharey=True, dpi=120)\n",
    "print(type(axes), type(axes.ravel()))\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签\n",
    "\n",
    "# 定义要使用的颜色与标记\n",
    "colors = {0:'g',1:'b',2:'r',3:'y'}\n",
    "markers={0:'o',1:'x',2:'*', 3:'p'}\n",
    "\n",
    "# 绘制各个子图\n",
    "for i, ax in enumerate(axes.ravel()):\n",
    "    ax.plot(sorted(randint(0,10,10)),sorted(randint(0,10,10)), marker=markers[i], color=colors[i])\n",
    "    ax.set_title('Ax'+str(i))\n",
    "    ax.yaxis.set_ticks_position('none')\n",
    "\n",
    "plt.suptitle('有4幅子图的图形', verticalalignment='bottom', fontsize=16)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d037f08c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "375"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "375"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4df6e465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1,2,3,4])\n",
    "type(axes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0764236",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
